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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
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一个复杂值的SAR基础模型,基于物理启发的表示学习.

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    本研究引入了一种新的遥感基础模型,使用复杂值合成孔径雷达 (SAR) 数据. 该模型增强了可解释性,并在下游任务中获得最先进的结果,即使数据有限.

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    科学领域:

    • 遥感 遥感 遥感 遥感
    • 计算机视觉 计算机视觉
    • 地质物理学 地质物理学

    背景情况:

    • 基础模型在遥感任务中表现出色,因为它们具有概括能力.
    • 合成孔径雷达 (SAR) 提供了关键的全天候,全天地球观测数据.
    • 现有的SAR解释模型在信息利用和可解释性方面面临挑战.

    研究的目的:

    • 为复杂值SAR数据开发一个物理可解释的遥感基础模型.
    • 改进SAR图像解释中的信息利用和概括.

    主要方法:

    • 一个新的基础模型通过模拟复杂值SAR数据上的极度分解进行预训练.
    • 使用散射查询来表示与SAR特征相互作用的散射基.
    • 雇佣的极度测量分解损失和功率自主监督损失用于指导式预训练.

    主要成果:

    • 在九个不同的下游解释任务中实现了最先进的性能.
    • 证明了稳定的特征表示提取和强大的泛化能力.
    • 即使在数据稀缺的场景中也表现出有效性.

    结论:

    • 拟议的基础模型有效地将物理解释性与高级深度学习相结合.
    • 该模型为SAR图像分析和地球观测应用提供了重大进展.
    • 这种方法在具有挑战性的条件下显示出对强大的远程传感智能的承诺.